Committee machine that votes for similarity between materials

TL;DR

This paper introduces a novel similarity measurement method for materials based on physical properties, utilizing clustering and committee machine techniques to improve understanding and prediction of material behaviors.

Contribution

The paper presents a new similarity measurement approach combining non-linear regression, clustering, and committee machine methods for materials analysis.

Findings

Meaningful cluster structures were identified.

Prediction accuracy of physical properties was significantly improved.

The similarity measure proved rational and effective.

Abstract

We developed a method for measuring the similarity between materials, focusing on specific physical properties. The obtained information can be utilized to understand the underlying mechanisms and to support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on non-linear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three datasets of well-characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, we focus on the formation energy, lattice parameter, and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpreting the mechanism, and an improvement of regression models is introduced for predicting the physical properties of the materials. Our experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials physical properties can be significantly increased, confirming the rationality of the proposed similarity measure.